Volunteer Summary

Table

Text

sets

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict sets with group and time_point (formula: sets ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.39) and the part related to the fixed effects alone (marginal R2) is of 0.02. The model’s intercept, corresponding to group = control and time_point = 1st, is at 19.02 (95% CI [18.49, 19.55], t(214) = 70.57, p < .001). Within this model:

• The effect of group [treatment] is statistically non-significant and positive (beta = 0.35, 95% CI [-0.38, 1.09], t(214) = 0.94, p = 0.345; Std. beta = 0.17, 95% CI [-0.19, 0.53])
• The effect of time point [2nd] is statistically non-significant and negative (beta = -0.09, 95% CI [-0.69, 0.52], t(214) = -0.28, p = 0.781; Std. beta = -0.04, 95% CI [-0.34, 0.25])
• The interaction effect of time point [2nd] on group [treatment] is statistically non-significant and positive (beta = 0.38, 95% CI [-0.49, 1.26], t(214) = 0.86, p = 0.392; Std. beta = 0.19, 95% CI [-0.24, 0.61])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

setv

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict setv with group and time_point (formula: setv ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.55) and the part related to the fixed effects alone (marginal R2) is of 5.58e-03. The model’s intercept, corresponding to group = control and time_point = 1st, is at 11.03 (95% CI [10.61, 11.46], t(214) = 50.46, p < .001). Within this model:

• The effect of group [treatment] is statistically non-significant and positive (beta = 0.21, 95% CI [-0.39, 0.80], t(214) = 0.68, p = 0.495; Std. beta = 0.12, 95% CI [-0.23, 0.48])
• The effect of time point [2nd] is statistically non-significant and positive (beta = 0.32, 95% CI [-0.10, 0.74], t(214) = 1.48, p = 0.139; Std. beta = 0.19, 95% CI [-0.06, 0.44])
• The interaction effect of time point [2nd] on group [treatment] is statistically non-significant and negative (beta = -0.24, 95% CI [-0.85, 0.37], t(214) = -0.77, p = 0.442; Std. beta = -0.14, 95% CI [-0.51, 0.22])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

maks

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict maks with group and time_point (formula: maks ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.68) and the part related to the fixed effects alone (marginal R2) is of 5.68e-03. The model’s intercept, corresponding to group = control and time_point = 1st, is at 44.67 (95% CI [43.72, 45.63], t(214) = 91.78, p < .001). Within this model:

• The effect of group [treatment] is statistically non-significant and positive (beta = 0.49, 95% CI [-0.84, 1.82], t(214) = 0.72, p = 0.470; Std. beta = 0.13, 95% CI [-0.23, 0.49])
• The effect of time point [2nd] is statistically non-significant and negative (beta = -0.23, 95% CI [-1.03, 0.57], t(214) = -0.57, p = 0.571; Std. beta = -0.06, 95% CI [-0.28, 0.15])
• The interaction effect of time point [2nd] on group [treatment] is statistically non-significant and positive (beta = 0.04, 95% CI [-1.11, 1.20], t(214) = 0.08, p = 0.940; Std. beta = 0.01, 95% CI [-0.30, 0.33])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

ibs

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict ibs with group and time_point (formula: ibs ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.67) and the part related to the fixed effects alone (marginal R2) is of 0.01. The model’s intercept, corresponding to group = control and time_point = 1st, is at 15.41 (95% CI [14.82, 16.01], t(214) = 51.04, p < .001). Within this model:

• The effect of group [treatment] is statistically non-significant and positive (beta = 0.05, 95% CI [-0.77, 0.88], t(214) = 0.13, p = 0.898; Std. beta = 0.02, 95% CI [-0.33, 0.38])
• The effect of time point [2nd] is statistically non-significant and positive (beta = 0.17, 95% CI [-0.33, 0.67], t(214) = 0.67, p = 0.503; Std. beta = 0.07, 95% CI [-0.14, 0.29])
• The interaction effect of time point [2nd] on group [treatment] is statistically non-significant and positive (beta = 0.40, 95% CI [-0.32, 1.12], t(214) = 1.08, p = 0.279; Std. beta = 0.17, 95% CI [-0.14, 0.49])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

ers_e

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict ers_e with group and time_point (formula: ers_e ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.62) and the part related to the fixed effects alone (marginal R2) is of 0.02. The model’s intercept, corresponding to group = control and time_point = 1st, is at 12.14 (95% CI [11.77, 12.50], t(214) = 65.35, p < .001). Within this model:

• The effect of group [treatment] is statistically non-significant and positive (beta = 0.15, 95% CI [-0.35, 0.66], t(214) = 0.59, p = 0.555; Std. beta = 0.11, 95% CI [-0.25, 0.46])
• The effect of time point [2nd] is statistically non-significant and negative (beta = -0.29, 95% CI [-0.63, 0.04], t(214) = -1.72, p = 0.086; Std. beta = -0.21, 95% CI [-0.44, 0.03])
• The interaction effect of time point [2nd] on group [treatment] is statistically non-significant and positive (beta = 0.38, 95% CI [-0.10, 0.86], t(214) = 1.54, p = 0.123; Std. beta = 0.27, 95% CI [-0.07, 0.61])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

ers_r

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict ers_r with group and time_point (formula: ers_r ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.31) and the part related to the fixed effects alone (marginal R2) is of 0.02. The model’s intercept, corresponding to group = control and time_point = 1st, is at 11.02 (95% CI [10.63, 11.40], t(214) = 56.42, p < .001). Within this model:

• The effect of group [treatment] is statistically non-significant and positive (beta = 0.18, 95% CI [-0.36, 0.71], t(214) = 0.65, p = 0.516; Std. beta = 0.12, 95% CI [-0.24, 0.48])
• The effect of time point [2nd] is statistically non-significant and positive (beta = 0.10, 95% CI [-0.37, 0.56], t(214) = 0.42, p = 0.675; Std. beta = 0.07, 95% CI [-0.25, 0.38])
• The interaction effect of time point [2nd] on group [treatment] is statistically non-significant and positive (beta = 0.26, 95% CI [-0.41, 0.93], t(214) = 0.76, p = 0.445; Std. beta = 0.17, 95% CI [-0.27, 0.62])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

pss_pa

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict pss_pa with group and time_point (formula: pss_pa ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.54) and the part related to the fixed effects alone (marginal R2) is of 0.01. The model’s intercept, corresponding to group = control and time_point = 1st, is at 44.47 (95% CI [43.33, 45.60], t(214) = 76.70, p < .001). Within this model:

• The effect of group [treatment] is statistically non-significant and positive (beta = 0.29, 95% CI [-1.29, 1.87], t(214) = 0.36, p = 0.717; Std. beta = 0.07, 95% CI [-0.29, 0.43])
• The effect of time point [2nd] is statistically non-significant and negative (beta = -1.09, 95% CI [-2.23, 0.05], t(214) = -1.87, p = 0.062; Std. beta = -0.25, 95% CI [-0.50, 0.01])
• The interaction effect of time point [2nd] on group [treatment] is statistically non-significant and positive (beta = 0.53, 95% CI [-1.11, 2.18], t(214) = 0.64, p = 0.525; Std. beta = 0.12, 95% CI [-0.25, 0.49])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

pss_ps

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict pss_ps with group and time_point (formula: pss_ps ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.70) and the part related to the fixed effects alone (marginal R2) is of 4.93e-03. The model’s intercept, corresponding to group = control and time_point = 1st, is at 26.67 (95% CI [24.59, 28.75], t(214) = 25.12, p < .001). Within this model:

• The effect of group [treatment] is statistically non-significant and negative (beta = -0.06, 95% CI [-2.95, 2.84], t(214) = -0.04, p = 0.968; Std. beta = -7.37e-03, 95% CI [-0.37, 0.35])
• The effect of time point [2nd] is statistically non-significant and positive (beta = 0.98, 95% CI [-0.70, 2.67], t(214) = 1.14, p = 0.254; Std. beta = 0.12, 95% CI [-0.09, 0.33])
• The interaction effect of time point [2nd] on group [treatment] is statistically non-significant and negative (beta = -1.59, 95% CI [-4.03, 0.86], t(214) = -1.27, p = 0.203; Std. beta = -0.20, 95% CI [-0.50, 0.11])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

pss

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict pss with group and time_point (formula: pss ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.70) and the part related to the fixed effects alone (marginal R2) is of 7.55e-03. The model’s intercept, corresponding to group = control and time_point = 1st, is at 45.21 (95% CI [42.25, 48.16], t(214) = 29.96, p < .001). Within this model:

• The effect of group [treatment] is statistically non-significant and negative (beta = -0.35, 95% CI [-4.47, 3.76], t(214) = -0.17, p = 0.867; Std. beta = -0.03, 95% CI [-0.39, 0.33])
• The effect of time point [2nd] is statistically non-significant and positive (beta = 2.09, 95% CI [-0.30, 4.49], t(214) = 1.71, p = 0.087; Std. beta = 0.18, 95% CI [-0.03, 0.39])
• The interaction effect of time point [2nd] on group [treatment] is statistically non-significant and negative (beta = -2.07, 95% CI [-5.54, 1.40], t(214) = -1.17, p = 0.242; Std. beta = -0.18, 95% CI [-0.48, 0.12])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

rki_responsible

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict rki_responsible with group and time_point (formula: rki_responsible ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.65) and the part related to the fixed effects alone (marginal R2) is of 1.15e-03. The model’s intercept, corresponding to group = control and time_point = 1st, is at 20.95 (95% CI [19.88, 22.02], t(214) = 38.38, p < .001). Within this model:

• The effect of group [treatment] is statistically non-significant and positive (beta = 0.12, 95% CI [-1.37, 1.60], t(214) = 0.15, p = 0.878; Std. beta = 0.03, 95% CI [-0.33, 0.39])
• The effect of time point [2nd] is statistically non-significant and negative (beta = -0.02, 95% CI [-0.95, 0.92], t(214) = -0.03, p = 0.975; Std. beta = -3.68e-03, 95% CI [-0.23, 0.22])
• The interaction effect of time point [2nd] on group [treatment] is statistically non-significant and positive (beta = 0.25, 95% CI [-1.10, 1.60], t(214) = 0.37, p = 0.713; Std. beta = 0.06, 95% CI [-0.27, 0.39])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

rki_nonlinear

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict rki_nonlinear with group and time_point (formula: rki_nonlinear ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.61) and the part related to the fixed effects alone (marginal R2) is of 0.02. The model’s intercept, corresponding to group = control and time_point = 1st, is at 13.12 (95% CI [12.37, 13.87], t(214) = 34.37, p < .001). Within this model:

• The effect of group [treatment] is statistically non-significant and positive (beta = 0.35, 95% CI [-0.69, 1.39], t(214) = 0.65, p = 0.513; Std. beta = 0.12, 95% CI [-0.24, 0.48])
• The effect of time point [2nd] is statistically non-significant and negative (beta = -0.30, 95% CI [-0.99, 0.39], t(214) = -0.84, p = 0.398; Std. beta = -0.10, 95% CI [-0.34, 0.14])
• The interaction effect of time point [2nd] on group [treatment] is statistically non-significant and positive (beta = 0.95, 95% CI [-0.05, 1.95], t(214) = 1.87, p = 0.061; Std. beta = 0.33, 95% CI [-0.02, 0.67])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

rki_peer

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict rki_peer with group and time_point (formula: rki_peer ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.58) and the part related to the fixed effects alone (marginal R2) is of 1.66e-03. The model’s intercept, corresponding to group = control and time_point = 1st, is at 20.47 (95% CI [19.90, 21.03], t(214) = 71.39, p < .001). Within this model:

• The effect of group [treatment] is statistically non-significant and positive (beta = 0.21, 95% CI [-0.57, 0.99], t(214) = 0.53, p = 0.595; Std. beta = 0.10, 95% CI [-0.26, 0.46])
• The effect of time point [2nd] is statistically non-significant and positive (beta = 0.03, 95% CI [-0.51, 0.57], t(214) = 0.11, p = 0.914; Std. beta = 0.01, 95% CI [-0.23, 0.26])
• The interaction effect of time point [2nd] on group [treatment] is statistically non-significant and negative (beta = -0.09, 95% CI [-0.87, 0.68], t(214) = -0.23, p = 0.818; Std. beta = -0.04, 95% CI [-0.40, 0.32])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

rki_expect

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict rki_expect with group and time_point (formula: rki_expect ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.50) and the part related to the fixed effects alone (marginal R2) is of 0.03. The model’s intercept, corresponding to group = control and time_point = 1st, is at 4.60 (95% CI [4.33, 4.88], t(214) = 32.53, p < .001). Within this model:

• The effect of group [treatment] is statistically non-significant and positive (beta = 0.28, 95% CI [-0.10, 0.67], t(214) = 1.44, p = 0.150; Std. beta = 0.26, 95% CI [-0.09, 0.62])
• The effect of time point [2nd] is statistically non-significant and positive (beta = 0.17, 95% CI [-0.12, 0.46], t(214) = 1.15, p = 0.252; Std. beta = 0.16, 95% CI [-0.11, 0.42])
• The interaction effect of time point [2nd] on group [treatment] is statistically non-significant and positive (beta = 0.11, 95% CI [-0.31, 0.53], t(214) = 0.51, p = 0.609; Std. beta = 0.10, 95% CI [-0.28, 0.49])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

rki

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict rki with group and time_point (formula: rki ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.66) and the part related to the fixed effects alone (marginal R2) is of 0.02. The model’s intercept, corresponding to group = control and time_point = 1st, is at 59.14 (95% CI [57.53, 60.75], t(214) = 71.87, p < .001). Within this model:

• The effect of group [treatment] is statistically non-significant and positive (beta = 0.96, 95% CI [-1.28, 3.20], t(214) = 0.84, p = 0.402; Std. beta = 0.15, 95% CI [-0.21, 0.51])
• The effect of time point [2nd] is statistically non-significant and negative (beta = -0.12, 95% CI [-1.52, 1.28], t(214) = -0.16, p = 0.870; Std. beta = -0.02, 95% CI [-0.24, 0.21])
• The interaction effect of time point [2nd] on group [treatment] is statistically non-significant and positive (beta = 1.25, 95% CI [-0.78, 3.27], t(214) = 1.21, p = 0.227; Std. beta = 0.20, 95% CI [-0.12, 0.53])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

raq_possible

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict raq_possible with group and time_point (formula: raq_possible ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.50) and the part related to the fixed effects alone (marginal R2) is of 0.01. The model’s intercept, corresponding to group = control and time_point = 1st, is at 15.74 (95% CI [15.28, 16.21], t(214) = 66.46, p < .001). Within this model:

• The effect of group [treatment] is statistically non-significant and negative (beta = -0.16, 95% CI [-0.81, 0.49], t(214) = -0.49, p = 0.626; Std. beta = -0.09, 95% CI [-0.45, 0.27])
• The effect of time point [2nd] is statistically non-significant and negative (beta = -0.39, 95% CI [-0.88, 0.09], t(214) = -1.58, p = 0.114; Std. beta = -0.22, 95% CI [-0.48, 0.05])
• The interaction effect of time point [2nd] on group [treatment] is statistically non-significant and positive (beta = 0.70, 95% CI [-4.44e-03, 1.40], t(214) = 1.95, p = 0.051; Std. beta = 0.38, 95% CI [-2.45e-03, 0.77])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

raq_difficulty

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict raq_difficulty with group and time_point (formula: raq_difficulty ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.59) and the part related to the fixed effects alone (marginal R2) is of 3.83e-03. The model’s intercept, corresponding to group = control and time_point = 1st, is at 12.53 (95% CI [12.18, 12.89], t(214) = 70.01, p < .001). Within this model:

• The effect of group [treatment] is statistically non-significant and negative (beta = -0.23, 95% CI [-0.72, 0.26], t(214) = -0.92, p = 0.360; Std. beta = -0.17, 95% CI [-0.52, 0.19])
• The effect of time point [2nd] is statistically non-significant and negative (beta = -0.12, 95% CI [-0.45, 0.21], t(214) = -0.71, p = 0.476; Std. beta = -0.09, 95% CI [-0.33, 0.15])
• The interaction effect of time point [2nd] on group [treatment] is statistically non-significant and positive (beta = 0.21, 95% CI [-0.26, 0.69], t(214) = 0.88, p = 0.379; Std. beta = 0.16, 95% CI [-0.19, 0.51])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

raq

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict raq with group and time_point (formula: raq ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.59) and the part related to the fixed effects alone (marginal R2) is of 6.08e-03. The model’s intercept, corresponding to group = control and time_point = 1st, is at 28.28 (95% CI [27.53, 29.02], t(214) = 74.25, p < .001). Within this model:

• The effect of group [treatment] is statistically non-significant and negative (beta = -0.39, 95% CI [-1.43, 0.65], t(214) = -0.73, p = 0.463; Std. beta = -0.13, 95% CI [-0.49, 0.22])
• The effect of time point [2nd] is statistically non-significant and negative (beta = -0.50, 95% CI [-1.20, 0.20], t(214) = -1.39, p = 0.164; Std. beta = -0.17, 95% CI [-0.41, 0.07])
• The interaction effect of time point [2nd] on group [treatment] is statistically non-significant and positive (beta = 0.90, 95% CI [-0.11, 1.91], t(214) = 1.75, p = 0.080; Std. beta = 0.31, 95% CI [-0.04, 0.66])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

who

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict who with group and time_point (formula: who ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.67) and the part related to the fixed effects alone (marginal R2) is of 1.21e-04. The model’s intercept, corresponding to group = control and time_point = 1st, is at 14.62 (95% CI [13.47, 15.78], t(214) = 24.80, p < .001). Within this model:

• The effect of group [treatment] is statistically non-significant and positive (beta = 0.02, 95% CI [-1.58, 1.63], t(214) = 0.03, p = 0.976; Std. beta = 5.55e-03, 95% CI [-0.36, 0.37])
• The effect of time point [2nd] is statistically non-significant and negative (beta = -0.11, 95% CI [-1.08, 0.87], t(214) = -0.21, p = 0.832; Std. beta = -0.02, 95% CI [-0.25, 0.20])
• The interaction effect of time point [2nd] on group [treatment] is statistically non-significant and positive (beta = 0.06, 95% CI [-1.35, 1.47], t(214) = 0.08, p = 0.933; Std. beta = 0.01, 95% CI [-0.31, 0.33])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

phq

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict phq with group and time_point (formula: phq ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.78) and the part related to the fixed effects alone (marginal R2) is of 4.15e-03. The model’s intercept, corresponding to group = control and time_point = 1st, is at 3.66 (95% CI [2.69, 4.62], t(214) = 7.45, p < .001). Within this model:

• The effect of group [treatment] is statistically non-significant and positive (beta = 0.20, 95% CI [-1.14, 1.54], t(214) = 0.29, p = 0.770; Std. beta = 0.05, 95% CI [-0.31, 0.42])
• The effect of time point [2nd] is statistically non-significant and positive (beta = 0.12, 95% CI [-0.54, 0.78], t(214) = 0.35, p = 0.727; Std. beta = 0.03, 95% CI [-0.15, 0.21])
• The interaction effect of time point [2nd] on group [treatment] is statistically non-significant and positive (beta = 0.35, 95% CI [-0.61, 1.31], t(214) = 0.71, p = 0.475; Std. beta = 0.09, 95% CI [-0.16, 0.35])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

gad

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict gad with group and time_point (formula: gad ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.71) and the part related to the fixed effects alone (marginal R2) is of 1.87e-03. The model’s intercept, corresponding to group = control and time_point = 1st, is at 3.38 (95% CI [2.48, 4.28], t(214) = 7.39, p < .001). Within this model:

• The effect of group [treatment] is statistically non-significant and negative (beta = -0.30, 95% CI [-1.54, 0.95], t(214) = -0.47, p = 0.639; Std. beta = -0.09, 95% CI [-0.44, 0.27])
• The effect of time point [2nd] is statistically non-significant and negative (beta = -0.03, 95% CI [-0.74, 0.68], t(214) = -0.09, p = 0.931; Std. beta = -9.05e-03, 95% CI [-0.21, 0.19])
• The interaction effect of time point [2nd] on group [treatment] is statistically non-significant and positive (beta = 0.43, 95% CI [-0.60, 1.46], t(214) = 0.82, p = 0.412; Std. beta = 0.12, 95% CI [-0.17, 0.42])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

nb_pcs

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict nb_pcs with group and time_point (formula: nb_pcs ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.68) and the part related to the fixed effects alone (marginal R2) is of 1.66e-03. The model’s intercept, corresponding to group = control and time_point = 1st, is at 51.88 (95% CI [50.08, 53.68], t(214) = 56.38, p < .001). Within this model:

• The effect of group [treatment] is statistically non-significant and negative (beta = -0.46, 95% CI [-2.96, 2.05], t(214) = -0.36, p = 0.722; Std. beta = -0.07, 95% CI [-0.42, 0.29])
• The effect of time point [2nd] is statistically non-significant and negative (beta = -0.79, 95% CI [-2.29, 0.71], t(214) = -1.03, p = 0.304; Std. beta = -0.11, 95% CI [-0.33, 0.10])
• The interaction effect of time point [2nd] on group [treatment] is statistically non-significant and positive (beta = 0.72, 95% CI [-1.45, 2.90], t(214) = 0.65, p = 0.516; Std. beta = 0.10, 95% CI [-0.21, 0.41])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

nb_mcs

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict nb_mcs with group and time_point (formula: nb_mcs ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.62) and the part related to the fixed effects alone (marginal R2) is of 3.48e-03. The model’s intercept, corresponding to group = control and time_point = 1st, is at 50.20 (95% CI [48.02, 52.38], t(214) = 45.12, p < .001). Within this model:

• The effect of group [treatment] is statistically non-significant and positive (beta = 0.08, 95% CI [-2.95, 3.11], t(214) = 0.05, p = 0.959; Std. beta = 9.49e-03, 95% CI [-0.35, 0.37])
• The effect of time point [2nd] is statistically non-significant and positive (beta = 0.96, 95% CI [-1.03, 2.94], t(214) = 0.94, p = 0.345; Std. beta = 0.11, 95% CI [-0.12, 0.35])
• The interaction effect of time point [2nd] on group [treatment] is statistically non-significant and negative (beta = -1.52, 95% CI [-4.39, 1.35], t(214) = -1.04, p = 0.300; Std. beta = -0.18, 95% CI [-0.52, 0.16])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

Table